COOPER is a distributed MARL method that learns emergent reputation assessment rules and policies from rewards, shown on donation and coin games in grid worlds with adaptation across co-players and networks.
A com- prehensive survey on multi-agent cooperative decision-making: Scenarios, approaches, challenges and perspectives
10 Pith papers cite this work. Polarity classification is still indexing.
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CLOVER augments value decomposition with a GNN mixer whose weights depend on the realized wireless communication graph, proving permutation invariance, monotonicity, and greater expressiveness than QMIX while showing gains on Predator-Prey and Lumberjacks under p-CSMA channels.
OSPO trains optimal order dispatch policies for homogeneous AV fleets using only one-step group rewards, outperforming GRPO on a real ride-hailing dataset.
SIGMA builds a signed relational graph among LLM agents and uses conflict-aware message passing plus weighted aggregation to produce more consistent predictions than prior cooperative-assumption baselines.
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
PID applied to MLLMs identifies task-specific modality interaction profiles that generalize across models, extend to tri-modal cases, and yield initial performance gains via reweighting.
A shared consensus vector, generated before any action, lets cooperative agents act simultaneously and lets the whole joint policy be trained with single-agent PPO.
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
A survey that examines fragmentation in existing 6G security approaches, develops a cross-layer threat taxonomy, maps countermeasures, and identifies research gaps for trustworthy AI-native 6G ecosystems.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
citing papers explorer
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Learning to cooperate with emergent reputation via multi-agent reinforcement learning
COOPER is a distributed MARL method that learns emergent reputation assessment rules and policies from rewards, shown on donation and coin games in grid worlds with adaptation across co-players and networks.
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Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning
CLOVER augments value decomposition with a GNN mixer whose weights depend on the realized wireless communication graph, proving permutation invariance, monotonicity, and greater expressiveness than QMIX while showing gains on Predator-Prey and Lumberjacks under p-CSMA channels.
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One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms
OSPO trains optimal order dispatch policies for homogeneous AV fleets using only one-step group rewards, outperforming GRPO on a real ride-hailing dataset.
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Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling
SIGMA builds a signed relational graph among LLM agents and uses conflict-aware message passing plus weighted aggregation to produce more consistent predictions than prior cooperative-assumption baselines.
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Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
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Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition
PID applied to MLLMs identifies task-specific modality interaction profiles that generalize across models, extend to tri-modal cases, and yield initial performance gains via reweighting.
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Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
A shared consensus vector, generated before any action, lets cooperative agents act simultaneously and lets the whole joint policy be trained with single-agent PPO.
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Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
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Toward a Unified Security and Privacy Framework for AI-Native 6G Networks
A survey that examines fragmentation in existing 6G security approaches, develops a cross-layer threat taxonomy, maps countermeasures, and identifies research gaps for trustworthy AI-native 6G ecosystems.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.